scholarly journals Self-supervised contrastive learning for integrative single cell RNA-seq data analysis

2021 ◽  
Author(s):  
Wenkai Han ◽  
Yuqi Cheng ◽  
Jiayang Chen ◽  
Huawen Zhong ◽  
Zhihang Hu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Biological Diversity ◽  
Mononuclear Cells ◽  
Single Cells ◽  
Data Representation ◽  
Superior Performance ◽  
Peripheral Blood Mononuclear ◽  
Technical Noise ◽  
Downstream Analysis

Single-cell RNA-sequencing (scRNA-seq) has become a powerful tool to reveal the complex biological diversity and heterogeneity among cell populations. However, the technical noise and bias of the technology still have negative impacts on the downstream analysis. Here, we present a self-supervised Contrastive LEArning framework for scRNA-seq (CLEAR) profile representation and the downstream analysis. CLEAR overcomes the heterogeneity of the experimental data with a specifically designed representation learning task and thus can handle batch effects and dropout events. In the task, the deep learning model learns to pull together the representations of similar cells while pushing apart distinct cells, without manual labeling. It achieves superior performance on a broad range of fundamental tasks, including clustering, visualization, dropout correction, batch effect removal, and pseudo-time inference. The proposed method successfully identifies and illustrates inflammatory-related mechanisms in a COVID-19 disease study with 43,695 single cells from peripheral blood mononuclear cells. Further experiments to process a million-scale single-cell dataset demonstrate the scalability of CLEAR. This scalable method generates effective scRNA-seq data representation while eliminating technical noise, and it will serve as a general computational framework for single-cell data analysis.

scholarly journals Statistical significance of cluster membership for determination of cell identities in single cell genomics

2018 ◽  
Author(s):  
Neo Christopher Chung
Keyword(s):  
Feature Selection ◽  
Single Cell ◽  
Mononuclear Cells ◽  
Statistical Significance ◽  
Large Family ◽  
Peripheral Blood Mononuclear ◽  
Cluster Membership ◽  
Inclusion Probabilities ◽  
Spatio Temporal ◽  
Downstream Analysis

AbstractSingle cell RNA sequencing (scRNA-seq) allows us to dissect transcriptional heterogeneity arising from cellular types, spatio-temporal contexts, and environmental stimuli. Cell identities of samples derived from heterogeneous subpopulations are routinely determined by clustering of scRNA-seq data. Computational cell identities are then used in downstream analysis, feature selection, and visualization. However, how can we examine if cell identities are accurately inferred? To this end, we introduce non-parametric methods to evaluate cell identities by testing cluster memberships of single cell samples in an unsupervised manner. We propose posterior inclusion probabilities for cluster memberships to select and visualize samples relevant to subpopulations. Beyond simulation studies, we examined two scRNA-seq data - a mixture of Jurkat and 293T cells and a large family of peripheral blood mononuclear cells. We demonstrated probabilistic feature selection and improved t-SNE visualization. By learning uncertainty in clustering, the proposed methods enable rigorous testing of cell identities in scRNA-seq.

scholarly journals Sierra: discovery of differential transcript usage from polyA-captured single-cell RNA-seq data

2019 ◽  
Author(s):  
Ralph Patrick ◽  
David T. Humphreys ◽  
Vaibhao Janbandhu ◽  
Alicia Oshlack ◽  
Joshua W.K. Ho ◽  
...  
Keyword(s):  
Single Cell ◽  
Mononuclear Cells ◽  
Human Peripheral Blood ◽  
Cardiac Fibroblasts ◽  
Single Cells ◽  
Cell Types ◽  
Computational Pipeline ◽  
Rna Seq ◽  
Peripheral Blood Mononuclear ◽  
Mrna Isoform

AbstractHigh-throughput single-cell RNA-seq (scRNA-seq) is a powerful tool for studying gene expression in single cells. Most current scRNA-seq bioinformatics tools focus on analysing overall expression levels, largely ignoring alternative mRNA isoform expression. We present a computational pipeline, Sierra, that readily detects differential transcript usage from data generated by commonly used polyA-captured scRNA-seq technology. We validate Sierra by comparing cardiac scRNA-seq cell-types to bulk RNA-seq of matched populations, finding significant overlap in differential transcripts. Sierra detects differential transcript usage across human peripheral blood mononuclear cells and the Tabula Muris, and 3’UTR shortening in cardiac fibroblasts. Sierra is available at https://github.com/VCCRI/Sierra.

scholarly journals Targeting individual cells by barcode in pooled sequence libraries

2017 ◽  
Author(s):  
Navpreet Ranu ◽  
Alexandra-Chloé Villani ◽  
Nir Hacohen ◽  
Paul C. Blainey
Keyword(s):  
Single Cell ◽  
Mononuclear Cells ◽  
Single Cells ◽  
Dna Barcodes ◽  
Rna Seq ◽  
Enrichment Method ◽  
Peripheral Blood Mononuclear ◽  
Efficient Sampling ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

There is rising interest in applying single-cell transcriptome analysis and other single-cell sequencing methods to resolve differences between cells. Pooled processing of thousands of single cells is now routinely practiced by introducing cell-specific DNA barcodes early in cell processing protocols1-5. However, researchers must sequence a large number of cells to sample rare subpopulations6-8, even when fluorescence-activated cell sorting (FACS) is used to pre-enrich rare cell populations. Here, a new molecular enrichment method is used in conjunction with FACS enrichment to enable efficient sampling of rare dendritic cell (DC) populations, including the recently identified AXL+SIGLEC6+ (AS DCs) subset7, within a 10X Genomics single-cell RNA-Seq library. DC populations collectively represent 1-2% of total peripheral blood mononuclear cells (PBMC), with AS DC representing only 1-3% of human blood DCs and 0.01-0.06% of total PBMCs.

scholarly journals 1830. Single-cell Transcriptional Profiling Reveals an Immune Cell State Signature of Bacterial Sepsis

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S42-S42
Author(s):  
Miguel Reyes ◽  
Roby P Bhattacharyya ◽  
Michael Filbin ◽  
Kianna Billman ◽  
Thomas Eisenhaure ◽  
...  
Keyword(s):  
Single Cell ◽  
Bacterial Infections ◽  
Mononuclear Cells ◽  
Immune Cell ◽  
Single Cells ◽  
Transcriptional Profiling ◽  
Functional Characterization ◽  
Specific Cell ◽  
Peripheral Blood Mononuclear ◽  
Cell State

Abstract Background Despite intense efforts to understand the immunopathology of sepsis, no clinically reliable diagnostic biomarkers exist. Multiple whole-blood gene expression studies have sought sepsis-associated molecular signatures, but these have not yet resolved immune phenomena at the cellular level. Using single-cell RNA sequencing (scRNA-Seq) to profile peripheral blood mononuclear cells (PBMCs), we identified a novel cellular state enriched in patients with sepsis. Methods We performed scRNA-Seq on PBMCs from 26 patients with sepsis and 47 controls at two hospitals (mean age 57.5 years, SD 16.6; 54% male; 82% white), analyzing >200,000 single cells in total on a 10× Genomics platform. We identified immune cell states by stepwise clustering, first to identify the major immune cell types, then clustering each cell type into substates. Substate abundances were compared between cases and controls using the Wilcoxon rank-sum test. Results We identified 18 immune cell substates (Figure 1a), including a novel CD14+ monocyte substate (MS1) that is enriched in patients with sepsis (Figure 1b). The fractional abundance of the MS1 substate alone (ROC AUC 0.88) outperformed published bulk transcriptional signatures in identifying sepsis (AUC 0.68–0.82) across our clinical cohorts. Deconvolution of publicly available bulk transcriptional data to infer the abundance of the MS1 substate externally validated its accuracy in predicting sepsis of various etiologies across diverse geographic locations (Figure 1c), matching the best previously identified bulk signatures. Flow cytometry using cell surface markers unique to MS1 confirmed its marked expansion in sepsis, facilitating quantitation and isolation of this substate for further study. Conclusion This study demonstrates the utility of scRNA-Seq in discovering disease-associated cytologic signatures in blood and identifies a cell state signature for sepsis in patients with bacterial infections. This novel monocyte substate matched the performance of the best bulk transcriptional signatures in classifying patients as septic, and pointed to a specific cell state for further molecular and functional characterization of sepsis immunopathogenesis. Disclosures All Authors: No reported Disclosures.

scholarly journals High-throughput chromatin accessibility profiling at single-cell resolution

2018 ◽  
Author(s):  
Anja Mezger ◽  
Sandy Klemm ◽  
Ishminder Mann ◽  
Kara Brower ◽  
Alain Mir ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Mononuclear Cells ◽  
De Novo ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Peripheral Blood Mononuclear ◽  
Whole Cells ◽  
Chip Processing ◽  
On Chip

We have developed a high-throughput single-cell ATAC-seq (assay for transposition of accessible chromatin) method to measure physical access to DNA in whole cells. Our approach integrates fluorescence imaging and addressable reagent deposition across a massively parallel (5184) nano-well array, yielding a nearly 20-fold improvement in throughput (up to ~1800 cells/chip, 4-5 hour on-chip processing time) and cost (~98¢ per cell) compared to prior microfluidic implementations. We applied this method to measure regulatory variation in Peripheral Blood Mononuclear Cells (PBMCs) and show robust,de-novoclustering of single cells by hematopoietic cell type.

scholarly journals Combined protein and transcript single cell RNA sequencing reveals cardiovascular disease and HIV signatures

2020 ◽  
Author(s):  
Jenifer Vallejo ◽  
Ryosuke Saigusa ◽  
Rishab Gulati ◽  
Yanal Ghosheh ◽  
Christopher P. Durant ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Single Cell ◽  
Rna Sequencing ◽  
Mononuclear Cells ◽  
Nk Cell ◽  
Single Cells ◽  
T Cell Subsets ◽  
Peripheral Blood Mononuclear ◽  
Cholesterol Lowering ◽  
Increased Risk

AbstractBackgroundHIV-infected people have an increased risk of atherosclerosis-based cardiovascular disease (CVD), even when the HIV virus is fully controlled. Both chronic HIV infection and CVD are chronic inflammatory diseases. The interaction between these two diseases is not well understood.MethodsThe Women’s Interagency HIV Study (WIHS) collected peripheral blood mononuclear cells (PBMCs) and data on subclinical CVD defined by carotid artery ultrasound from HIV-infected women. We interrogated 32 PBMC samples using combined protein and transcript panel single cell (sc) RNA sequencing of women without HIV or CVD, with HIV only, with HIV and CVD, and with HIV and CVD treated with cholesterol-lowering drugs. Expression of 40 surface markers enabled detailed analysis of all major cell types, resolving 58 clusters in almost 42,000 single cells.ResultsMany clusters including 5 of 8 classical monocyte clusters showed significantly different gene expression between the groups of participants, revealing the inflammatory signatures of HIV, CVD and their interactions. Genes highly upregulated by CVD included CCL3, CCL4 and IL-32, whereas CXCL2 and 3 were more highly upregulated by HIV. Many genes were synergistically upregulated by HIV and CVD, but others were antagonistically regulated, revealing that the gene signature in people with HIV and CVD is not simply the sum of the HIV and CVD signatures. Elevated expression of most inflammatory genes was reversed by cholesterol control (statin treatment). The cell numbers in 3 of 5 intermediate monocyte subsets, 1 of 14 CD8 T cell subsets, 1 of 6 B cell subsets and 1 of 6 NK cell subsets showed significant changes with HIV or CVD.ConclusionsWe conclude that HIV and CVD show interactive inflammatory signatures including chemokines and cytokines that are improved by cholesterol-lowering drugs.

scholarly journals Single cell transcriptomic landscape of diabetic foot ulcers

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Georgios Theocharidis ◽  
Beena E. Thomas ◽  
Debasree Sarkar ◽  
Hope L. Mumme ◽  
William J. R. Pilcher ◽  
...  
Keyword(s):  
Single Cell ◽  
Diabetic Foot ◽  
Mononuclear Cells ◽  
Single Cells ◽  
Macrophage Polarization ◽  
Cell Types ◽  
Peripheral Blood Mononuclear ◽  
Preferential Localization ◽  
M1 Macrophage ◽  
Healing Wounds

AbstractDiabetic foot ulceration (DFU) is a devastating complication of diabetes whose pathogenesis remains incompletely understood. Here, we profile 174,962 single cells from the foot, forearm, and peripheral blood mononuclear cells using single-cell RNA sequencing. Our analysis shows enrichment of a unique population of fibroblasts overexpressing MMP1, MMP3, MMP11, HIF1A, CHI3L1, and TNFAIP6 and increased M1 macrophage polarization in the DFU patients with healing wounds. Further, analysis of spatially separated samples from the same patient and spatial transcriptomics reveal preferential localization of these healing associated fibroblasts toward the wound bed as compared to the wound edge or unwounded skin. Spatial transcriptomics also validates our findings of higher abundance of M1 macrophages in healers and M2 macrophages in non-healers. Our analysis provides deep insights into the wound healing microenvironment, identifying cell types that could be critical in promoting DFU healing, and may inform novel therapeutic approaches for DFU treatment.

scholarly journals Massively parallel digital transcriptional profiling of single cells

2016 ◽  
Author(s):  
Grace X.Y. Zheng ◽  
Jessica M. Terry ◽  
Phillip Belgrader ◽  
Paul Ryvkin ◽  
Zachary W. Bent ◽  
...  
Keyword(s):  
Single Cell ◽  
Mononuclear Cells ◽  
Single Cells ◽  
Biological Systems ◽  
Cell Encapsulation ◽  
Host Cells ◽  
Cell Capture ◽  
Transcriptome Data ◽  
Peripheral Blood Mononuclear

ABSTRACTCharacterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of up to tens of thousands of single cells per sample. Cell encapsulation in droplets takes place in ∼6 minutes, with ∼50% cell capture efficiency, up to 8 samples at a time. The speed and efficiency allow the processing of precious samples while minimizing stress to cells. To demonstrate the system′s technical performance and its applications, we collected transcriptome data from ∼¼ million single cells across 29 samples. First, we validate the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. Then, we profile 68k peripheral blood mononuclear cells (PBMCs) to demonstrate the system′s ability to characterize large immune populations. Finally, we use sequence variation in the transcriptome data to determine host and donor chimerism at single cell resolution in bone marrow mononuclear cells (BMMCs) of transplant patients. This analysis enables characterization of the complex interplay between donor and host cells and monitoring of treatment response. This high-throughput system is robust and enables characterization of diverse biological systems with single cell mRNA analysis.

scholarly journals Single-cell multi-omics analysis of the immune response in COVID-19

2021 ◽  
Author(s):  
Emily Stephenson ◽  
◽  
Gary Reynolds ◽  
Rachel A. Botting ◽  
Fernando J. Calero-Nieto ◽  
...  
Keyword(s):  
Immune Response ◽  
T Cells ◽  
Single Cell ◽  
Mononuclear Cells ◽  
Severe Disease ◽  
Effector Memory ◽  
Peripheral Blood Mononuclear ◽  
Cross Sectional ◽  
Hematopoietic Stem ◽  
Symptomatic Disease

AbstractAnalysis of human blood immune cells provides insights into the coordinated response to viral infections such as severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). We performed single-cell transcriptome, surface proteome and T and B lymphocyte antigen receptor analyses of over 780,000 peripheral blood mononuclear cells from a cross-sectional cohort of 130 patients with varying severities of COVID-19. We identified expansion of nonclassical monocytes expressing complement transcripts (CD16+C1QA/B/C+) that sequester platelets and were predicted to replenish the alveolar macrophage pool in COVID-19. Early, uncommitted CD34+ hematopoietic stem/progenitor cells were primed toward megakaryopoiesis, accompanied by expanded megakaryocyte-committed progenitors and increased platelet activation. Clonally expanded CD8+ T cells and an increased ratio of CD8+ effector T cells to effector memory T cells characterized severe disease, while circulating follicular helper T cells accompanied mild disease. We observed a relative loss of IgA2 in symptomatic disease despite an overall expansion of plasmablasts and plasma cells. Our study highlights the coordinated immune response that contributes to COVID-19 pathogenesis and reveals discrete cellular components that can be targeted for therapy.

scholarly journals Single-cell characterization of a model of poly I:C-stimulated peripheral blood mononuclear cells in severe asthma

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ailu Chen ◽  
Maria P. Diaz-Soto ◽  
Miguel F. Sanmamed ◽  
Taylor Adams ◽  
Jonas C. Schupp ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Severe Asthma ◽  
Mononuclear Cells ◽  
Effector T Cells ◽  
Poly I:C ◽  
Peripheral Blood Mononuclear ◽  
Main Cell ◽  
Blood Mononuclear Cells ◽  
Quantitative Changes

Abstract Background Asthma has been associated with impaired interferon response. Multiple cell types have been implicated in such response impairment and may be responsible for asthma immunopathology. However, existing models to study the immune response in asthma are limited by bulk profiling of cells. Our objective was to Characterize a model of peripheral blood mononuclear cells (PBMCs) of patients with severe asthma (SA) and its response to the TLR3 agonist Poly I:C using two single-cell methods. Methods Two complementary single-cell methods, DropSeq for single-cell RNA sequencing (scRNA-Seq) and mass cytometry (CyTOF), were used to profile PBMCs of SA patients and healthy controls (HC). Poly I:C-stimulated and unstimulated cells were analyzed in this study. Results PBMCs (n = 9414) from five SA (n = 6099) and three HC (n = 3315) were profiled using scRNA-Seq. Six main cell subsets, namely CD4 + T cells, CD8 + T cells, natural killer (NK) cells, B cells, dendritic cells (DCs), and monocytes, were identified. CD4 + T cells were the main cell type in SA and demonstrated a pro-inflammatory profile characterized by increased JAK1 expression. Following Poly I:C stimulation, PBMCs from SA had a robust induction of interferon pathways compared with HC. CyTOF profiling of Poly I:C stimulated and unstimulated PBMCs (n = 160,000) from the same individuals (SA = 5; HC = 3) demonstrated higher CD8 + and CD8 + effector T cells in SA at baseline, followed by a decrease of CD8 + effector T cells after poly I:C stimulation. Conclusions Single-cell profiling of an in vitro model using PBMCs in patients with SA identified activation of pro-inflammatory pathways at baseline and strong response to Poly I:C, as well as quantitative changes in CD8 + effector cells. Thus, transcriptomic and cell quantitative changes are associated with immune cell heterogeneity in this model to evaluate interferon responses in severe asthma.

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